The empirical basis
While a large number of altitude-resolved H2O records inferred from
limb emission or occultation measurements e.g.,
as well as merged data sets e.g. exist, for this
study, stratospheric H2O records from HALOE and MIPAS
have been used. The reason is that both these instruments
provided H2O measurements at near-global coverage and that their mission
periods were nicely complementary, with a sufficiently long overlap period
for data harmonization. Inclusion of further instruments would have implied
an additional risk of artefacts due to unknown differences in data
characteristics.
HALOE
The Halogen Occultation Instrument (HALOE) is a solar
occultation infrared radiometer for the measurement of composition and
temperature of the middle atmosphere. It recorded atmospherically attenuated
solar radiance in four channels between 996 and 4081 cm-1. HALOE was a
payload of the Upper Atmosphere Research Satellite (UARS) and was operational
from 11 October 1991 to 21 November 2005. With about 15 UARS orbits per day
and one sunrise and one sunset measurement per orbit, up to about 10 800
vertical profiles of each target quantity could be measured per year. One of
the target species measured by HALOE is H2O, for which an altitude
resolution of 2 to 3 km is reported . In
this work we use HALOE data version 19, which was discussed in
and , where a small dry bias is
reported for the altitude range relevant to this paper. Problems with HALOE
water vapour retrievals of an earlier data version due to aerosol have been
reported by but problematic cases discussed there were no
longer present in the data set we used and thus seem to have been removed
. During its 14-year lifetime, HALOE H2O measurements
were frequently validated . No
significant instrumental drifts were found by when
they compared HALOE time series with those from various independent
measurements.
MIPAS
The Michelson Interferometer for Passive Atmospheric Sounding
MIPAS, is a limb emission mid-infrared Fourier
transform spectrometer designed for limb-sounding of the composition and
temperature of the middle atmosphere. Its spectral coverage is 685 to
2410 cm-1. MIPAS was a core instrument of the Envisat research
satellite which was launched into a polar sun-synchronous orbit on
1 March 2002. The MIPAS data record covers the time from July 2002 to
April 2012, with a data gap in 2004. In the first part of the mission
(2002–2004) MIPAS recorded high-resolution (HR) spectra (apodized resolution
0.05 cm-1). In March 2004 operation was interrupted due to problems
with the interferometer slide until in January 2005 operation was resumed,
however at reduced spectral resolution (RR, 0.121 cm-1 after
apodization). In turn, the shorter optical path difference associated with
the reduced spectral resolution measurements allowed for a denser tangent
altitude grid and along with this a better vertical resolution, which is
4.0 km in the middle stratosphere as opposed to 4.5 km for the high
spectral resolution measurements. With 14.4 orbits per day and 74 (96) limb
scans per orbit in HR (RR) mode, MIPAS recorded 1065 (1382) profiles per day.
The MIPAS H2O data used here were produced with a dedicated research
processor developed and operated by the Institute of Meteorology and Climate
Research (IMK) team in Karlsruhe, Germany, in cooperation with the Instituto
de Astrofísica de Andalucía-CSIC in Granada, Spain
. The MIPAS H2O retrieval and validation is
reported in , and
. In this paper we have used data versions V5h_H2O_20
for the HR measurements and V5r_H2O_220/221 for the RR measurements.
Versions 220 and 221 are scientifically equivalent but carry different
version numbers to maintain traceability of data processing details.
The MIPAS instrument stability has been assessed (Michael Kiefer, personal
communication, 2015). A possible drift due to detector-aging and resulting
changes of its non-linear response was estimated at approximately
-0.05 ppmv decade-1. This is in agreement with, e.g.
who did not find any larger relative drifts
between the Water Vapor Millimeter-wave Spectrometer and various
satellite-borne instruments including MIPAS.
Regression analysis
In order to better understand the temporal variation of H2O in the lower
stratosphere, a multilinear regression analysis of the time series was
performed for each altitude/latitude bin. The regression model proposed
by and extended by
was used for this purpose. It optionally considers the use of the full data
error covariance matrix and represents the local volume mixing ratio of water
vapour as a function of time using a constant term, a linear
trend, amplitudes of various harmonic oscillations and user defined proxies as fit variables.
Piecewise linear trends as derived by the cumulative sum method following
or were tried but finally not
considered because they merely help to describe but not to explain the temporal
variation. For each harmonic, both the coefficients of the sine and the cosine
term are fitted, which together control both the phase and the amplitude of the
harmonic. The correlated part of the error is attributed to variations that are
not described in the regression model. The correlation coefficients of this
model error term are obtained from the residuals of a first iteration where
only the standard errors of the monthly mean mixing ratios were considered as
data errors. The amplitude of this additional error term was adjusted
iteratively to comply with χ2 statistics .
The merged time series (top panel, black curve) with the standard
errors of the data (black) and the best fitting standard regression model
(top panel, red curve) and the linear term of the regression (green line). In
the lower panel the residual time series between the measured data and the
fitted regression model is shown. The latitude bin of 0–10∘ S is
shown for an altitude of 17 km as an example. The residual
(rms = 0.35 ppmv) appears to have a systematic harmonic component with a
period of about 11 years.
The standard regression
Besides the constant and linear term, the annual cycle and its first three
overtones (wavenumbers two, three, and four waves per year) were considered.
Wavenumber two represents the semi-annual oscillations, and wavenumbers two
to four help to better model the annual cycle when it is not perfectly harmonic.
The following proxies were considered:
The quasi-biennial oscillation (QBO) was parametrized using Singapore winds
at 30 and 50 hPa, as obtained from the Institut für Meteorologie of the
Freie Universität Berlin,
(http://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo).
Between the winds at these pressure levels, there is a phase shift of
approximately π2. Thus, fitting coefficients of both of these
gives access to the approximate phase and amplitude of the QBO signal
cf., e.g..
For the El Niño–Southern Oscillation (ENSO) signal, the Multivariate ENSO
Index (MEI) (http://www.esrl.noaa.gov/psd/enso/mei/index.html) was used
as a proxy. Since this data set refers to a tropical surface pressure level,
a time lag was considered to make the proxy representative for the
stratospheric latitudes and altitudes considered here. To estimate the time
lag, temporally averaged stratospheric mean age of air data from
were used.
In the fitted time series there are pronounced systematic residuals. Some of
them are related to an apparent discontinuity in the water vapour abundance
in 2001, the well-known millennium water vapour drop
but the fits are unsatisfactory in the
entire period before 2007. The residual time series appears to be dominated
by a systematic harmonic feature of a period length of about 11 years.
Figure shows the fit of the time series at 17 km altitude in
the latitude bin 0–10∘ S as an example.
Consideration of the solar cycle
The fit residuals obtained by the regression analysis described in the
previous section resemble a harmonic with a period of about 11 years.
Besides, strong H2O decreases are visible in 1994 and 2001. The period of
11 years suggests also considering the solar cycle in the regression model.
Two approaches have been tried:
Approach 1: the solar cycle was modelled by a harmonic of 127 months with an
overtone of 63 months cf.. Fitting of the
related sine and cosine coefficients gave access to the amplitude and phase
of the solar signal. Consideration of the solar term improves the fits within
60∘ S–60∘ N in 92 % of the altitude/latitude bins
(Fig. ). The improvement is most pronounced at altitudes
around 25 km and reaches 20–30 % in some altitude/latitude bins. The time
series at 0–10∘S , 17 km altitude is shown as an example how the
new regression model fits the time series (Fig. ). While
both the H2O minimum in 1994 and the so-called millennium drop in 2001 are
still visible in the residual data and still calls for explanation, the
majority of the systematic residuals have disappeared and the general shape
of the time series is nicely reproduced by the regression model. This result
suggests that the solar cycle might indeed partially control lower
stratospheric water vapour.
The root mean square improvement of the fit residual with respect
to the standard approach, gained by the inclusion of the solar cycle
approximated by harmonic parametrization as described under Approach 1 in
Sect. 4.2. White bins are positive values, i.e. deterioration of the
fit.
Top panel: Fitted regression model with solar cycle approximated by
harmonic parametrization as described under Approach 1 in Sect. 4.2. The blue
curve is the fitted contribution of these harmonics and the green line is the
linear component. The middle panel (blue curve) shows the original solar
cycle F10.7 parametrization in arbitrary units. In the lower panel the
residual time series between the measured data and the fitted regression
model is shown. The rms for this fit is 0.30 ppmv. For further details, see
Fig. .
Approach 2: alternatively to the treatment with harmonics, the solar cycle
has been fitted using the radio flux index at a wavelength of 10.7 cm
(F10.7) as a proxy. This index, which is available via the Solar and
Heliospheric Observatory (SOHO,
http://sohowww.nascom.nasa.gov/sdb/ydb/indices_flux_raw/Penticton_Observed/monthly/MONTHPLT.OBS)
is proportional to solar activity. Since it is not a priori clear which
solar-terrestrial processes might control the H2O content of the
stratosphere and where exactly they happen, and how long the processed air
travels through the stratosphere before it is observed, the phase shift
obtained from Approach 1 (approximation of the solar cycle effect by harmonic
functions) has also been applied to the F10.7 proxy. Delayed anti-correlation
(lowest water vapour for solar maximum, shifted by several months, depending
on altitude and latitude) provided the best results. The improvements over
the regression without the solar term are shown in Fig. .
While the improvements are less pronounced in some of the bins than for
Approach 1, this approach seems to be more adequate for the inner tropical
lowermost stratosphere. For 95 % of the bins within
60∘ S–60∘ N the fit has been improved compared to the
standard approach without solar cycle. The altitude/latitude bin at
0–10∘ S, 17 km is shown as an example (Fig. ).
In this particular case, the residual due to the millennium drop is less
pronounced than in the case with the regression model using the harmonic
representation of the solar cycle effect, but it is still visible.
The root mean square improvement of the fit residual with respect
to the standard approach gained by the inclusion of the solar cycle
approximated by the F10.7 proxy as described under Approach 2 in Sect. 4.2.
White bins are positive values, i.e. deterioration of the
fit.
Both approaches reveal a strong relation between the water vapour abundances and
the solar cycle. The correlation is phase-shifted in a sense that lowest water
vapour abundances are seen a couple of years after the solar maximum
(see Fig. as an example).
The amplitudes of the solar component in the regression model are shown in
Fig. for both the harmonic (top panel) and the
F10.7 (bottom panel) parametrization. While the amplitudes associated with
the harmonic approach are larger, the altitude/latitude distributions of the
amplitudes associated with each approach have the same structure. Largest
effects are seen around the tropical tropopause region, and smallest in the
southern mid-latitudinal middle stratosphere.
The propagation of the data errors through the regression model leads to
uncertainties of these amplitudes of generally less than 2 % within the
tropical pipe and less than 5 % outside. Fit residuals, however, are not
compliant with χ2 statistics, indicating that the regression model, even
with the solar term included, is less than perfect and does not fully describe
the entire variation of stratospheric H2O. Analysis of the fit residuals
and consideration of resulting estimates of correlated model errors suggests
an uncertainty in the order of 15 to 50 % over a larger part of the
altitude/latitude range, with highest and contiguous significance (15–25 %
relative error of the amplitude) in the tropical tropopause range. This
provides good confidence in the results.
Top panel: fitted regression model with solar cycle approximated by
the F10.7 proxy as described under Approach 2 in Sect. 4.2. The blue curve is
the fitted solar signal contribution with the F10.7 proxy. The middle panel
(blue curve) shows the original solar cycle F10.7 parametrization in
arbitrary units. In the lower panel the residual time series between the
measured data and the fitted regression model is shown. The rms for this fit
is 0.31 ppmv. For further details, see
Fig. .
“Quasi-amplitudes” of fitted terms representing the solar cycle in
the regression, i.e. the halved differences between the maxima and minima
along the time series of these contributions. Top panel: harmonic
parametrization; lower panel: F10.7
parametrization.
The phase shift of the solar signal (Fig. ) is an interesting
result in itself because it helps to determine where in the atmosphere the
solar-terrestrial processes controlling the stratospheric H2O content might
take place. The phase shift α – which, for all altitude/latitude
bins, represents a delay of the negative response of water vapour to the
original solar cycle – is about 40 months at about 18 km altitude and 45 to
50 months at about 22 km altitude in the inner tropics. This implies that a
certain phase α which is seen at a certain time at, e.g. 18 km
altitude, is observed 5 to 10 months later at 22 km altitude. We compare
this with the temporally averaged mean age of stratospheric air distribution
(age(ϕ,z)) by , which is a measure of the
Brewer–Dobson circulation. This data set, although for a shorter period, is
the only available global observational climatology of age of air. Since the
age of air changes only slowly , we consider the
temporal average for 2002–2010 as approximately representative for the full
period. The increase of the age of air between 18 and 22 km also is 5 to
10 months, giving the following relation.
The distribution of the phase shift between the solar maximum and
negative water vapour response over latitude and altitude. Positive phase
shifts represent a delay of the response of water vapour to the solar
cycle.
α(ϕ,z)-α(0∘,18km)≈age(ϕ,z)-age(0∘,18km)
This suggests that the solar effect is not a local one but that part of the
phase shift might be caused by transport processes via the upwelling branch
of the Brewer–Dobson circulation of a signal generated near the tropical
tropopause. Further, the fact that the phase shift is larger than the age of
air in the lowermost stratosphere suggests that the effect itself must have
an inherent time lag (inh.lag). It can be estimated from the difference of
the phase shift of the solar signal and the age of stratospheric air,
assuming that the solar perturbation is transported from the tropical
tropopause region into the stratosphere by the stratospheric residual
circulation:
inh.lag(ϕ,z)=α(ϕ,z)-age(ϕ,z).
The inherent time lags as a function of latitude and altitude are shown in
Fig. . We find that for all points below the triangle defined by
the points (60∘ S, 15 km), (0∘, 23 km) and
(60∘ N, 15 km) the inherent time lag is almost constant and amounts
to roughly 25 months (extrema are 15 and 30 months). A slight decrease of the
inherent time lag with altitude, particularly in the tropical pipe, can be
explained as follows. It is well-known that the mean age of stratospheric air
overestimates the pure transit time of a signal and that
in the tropical pipe the discrepancy between age of air and transit time
increases with altitude. Thus, the correction by age of air is too large and
increases with altitude.
For higher altitudes and latitudes, the phase shift shows a different
behaviour. After having reached a maximum in the lower stratosphere
(green/yellow belt in Fig. ), the phase shift becomes smaller
again. Moreover the inherent time lag is negative and decreases further with
altitude and latitude. This hints at different processes governing the solar
cycle response of water vapour at higher altitudes.
Inherent time lag of the solar signal in water vapour, i.e.
difference of the phase shift of the solar signal in water vapour and the age
of stratospheric air as derived in . Positive values
represent delays of the solar signal in water vapour larger than the
stratospheric mean age of air.
Implication for the linear trends and other regression parameters
Inclusion of a solar cycle by either approach discussed in Sect. 4.2 has
improved the fit of the regression model to the measured H2O time series.
Inclusion of the solar component has largely reduced the systematic residuals
of the fit of the time series. When the F10.7 proxy was used, even the
millennium drop was – coincidentally or not – modelled much better.
Regardless of if a causal relation between solar activity and the lower
stratospheric H2O distribution is claimed or not, any missing descriptive
term in an incomplete regression model causes residuals which are aliased
onto other parameters in the fit. In the case discussed here, inclusion of
the solar cycle terms leads to much more negative water vapour trends and in
some altitude/latitude bins even changes the sign of the trend
(Fig. ). In the standard regression model stratospheric water
vapour abundances increase or decrease by less than 0.2 ppmv decade-1
nearly everywhere. In particular, a contiguous increase in the lower
stratosphere in the order of 0.1–0.2 ppmv decade-1 is seen. When the
solar cycle is considered, stratospheric water vapour decreases everywhere,
and stronger than by -0.1 ppmv decade-1 at most latitudes and
altitudes. This indicates that, even if one does not believe the solar cycle
effect in explanatory terms, it still is important in descriptive terms in
order to avoid artefacts caused by the related systematic residuals. This
means that the related systematic residuals, whatever their cause may be, can
emulate artificial trend components. Systematic effects on the annual and
semiannual cycles as well as QBO and ENSO amplitudes are much less
pronounced.
Linear terms of the multivariate regression of water vapour time
series with and without the inclusion of a solar term in the regression
model. Top panel: standard approach without solar term; lower panel:
including F10.7 parametrization.
Discussion
The analysis of the merged MIPAS–HALOE time series by multivariate linear
regression, including a solar cycle proxy as described above, suggests that a
solar signal is imprinted on the water vapour abundance entering the
stratosphere at the tropical tropopause, and this signal is then transported
to the middle stratosphere via the Brewer–Dobson circulation. The signal
vanishes in the middle stratosphere. The solar signal in the water vapour
time series is phase-shifted anti-correlated to the solar cycle, i.e. lowest
water vapour after solar maximum is found. The phase shift consists of two
components: the first component is an inherent time lag of about 25 months;
the second component results from transport times in the stratosphere by the
Brewer–Dobson circulation as approximated by the mean age of air.
Two obvious candidates to explain a solar signal in lower stratospheric water
vapour are methane oxidation and the import of water vapour through the
tropical tropopause into the stratosphere.
The photochemical oxidation of methane is an important contribution to the
stratospheric water vapour budget . However, the efficiency
of the conversion increases with altitude, and this is opposite to the solar
cycle variation observed here (see Fig. ). The
variations of methane in the tropical lower stratosphere are very small (less
than 0.1 ppmv, not shown here) and not sufficient to explain the observed
variation in lower stratospheric water vapour.
The import of water vapour from the troposphere into the stratosphere is to the
first order controlled by the tropical cold-point temperature which implies
that any mechanism leading to solar cycle influence on the tropical tropopause
temperatures could explain the solar cycle signal in water vapour.
Different studies exist that analyse the influence of the solar cycle
onto the tropical tropopause temperature with different results:
investigated NH winters, when the lowest temperatures
and water vapour entry values are observed in the lower stratosphere. They
used a trajectory model fed with input from ECMWF. In a zonal average they
found 0.2 K higher cold-point temperatures during solar maximum as compared
to solar minimum which would contradict our findings. However, over the
western Pacific, where most of the air experiences its final dehydration
, they found a stronger negative temperature
anomaly in the order of 1 K for solar maximum. For solar minimum, a
respective positive temperature anomaly of 1 K was found.
To put these results into context of our observations, we have estimated the
temperature variation necessary to produce our observed solar-cycle-driven
water vapour variations. Using the relation between temperature and
saturation vapour pressure, such 2 K variation corresponds to a variation in
water vapour of about 1 to 1.5 ppmv, assuming long-term average temperature
conditions for the tropical cold-point tropopause (∼ 191 K). This
would be more than sufficient to explain the solar variation observed in
water vapour. However, for temperatures below 187 K, as typical for the NH
winter season, a 2 K variation would result in water vapour variations that
cannot explain the observed variation. In this estimation we explicitly
assumed a constant saturation level of 100 %, which may be not appropriate.
As a second approach to estimate the temperature variations needed to explain
our observed water vapour variations, a regression of observed water vapour
variations at the tropical tropopause (ΔH2O) and variations of
approximate cold-point temperatures (ΔT), the latter derived from
radio occultation observations, were evaluated. This yields the following
linear relationship:
{ΔH2O}ppmv=0.23⋅{ΔT}K+0.01,
where curly brackets indicate numerical values. According to these data, the
observed solar component of the water vapour variation would require a
peak-to-peak cold-point temperature variation of about 3 K, which is larger
than the variations found by .
In contrast to , reported higher
temperatures during solar maximum right above the tropical tropopause and lower
temperatures right below the tropopause. However, there was no obvious
response at the tropopause itself.
used Whole Atmosphere Community Model (WACCM) 3.5
simulations from 1960–2004 to study the solar cycle influence. The analysis
indicated that there was a positive correlation between solar cycle and
stratospheric temperature; however, large parts could be attributed to the
alignment of the solar cycle with Pinatubo and El Chichon eruptions. They
concluded that it is very difficult to unambiguously assign the variability
to the solar cycle. Typically they found a lag of 1 year between the lower
stratospheric temperature response and the solar forcing (averaged over
25∘ S–25∘ N). This is different from our results where the
time lag is much larger. could extract a robust signal
only above 10 hPa, while below 10 hPa, the ambiguity between volcanic influence and
solar cycle was too pronounced.
Both the “top-down” solar influence based on solar heating of the
stratosphere and the “bottom-up” mechanism (based on solar heating of the
sea surface and dynamically coupled air–sea interaction) strengthen the
tropical convection and produce an amplified sea surface temperature (SST),
precipitation, and cloud response in the tropical Pacific to a relatively
small solar forcing (see ). These authors found
further that an east Pacific sea surface temperature cooling during the solar
maximum is followed by a sea surface temperature warming over wide areas of
the Pacific about 2 years later.
According to and in agreement with and
, globally averaged SST anomalies show highest correlations
with solar activity with a phase shift of 1–2 years.
found that the eastern tropical Pacific warm phase of the 11-year cycle
lagged the peak solar forcing by 1–3 years. All these results are in good
agreement with the inherent lag identified in the solar signal in the water
vapour time series.
Assuming that the cause of the solar signal seen in water vapour comes from
the ocean, provided an explanation of how
the signal is transported from the ocean to the lower stratosphere. Higher
sea surface temperatures amplify deep convection locally. The latent heat
release from the convection induces pressure perturbations which in turn
manifest themselves in the excitation of quasi-stationary planetary waves.
These move upwards through the easterly winds, dissipate, but are still
strong enough to induce a strengthening of the upwelling. Increased upwelling
leads to lower tropopause temperatures and reduced water vapour. Since
enhanced sea surface temperatures are found about 2 years after the solar
maximum , this would explain the water vapour
minimum found 2 years after the solar maximum in our study. The cold Pacific
during the solar maximum would act towards reduction of upwelling, leading to
higher tropopause temperature and higher water vapour concentrations during
solar maximum. The process described by
happens during summer (June to September in the Northern Hemisphere and
between December and March in the Southern Hemisphere), i.e. not during the
times when the Brewer–Dobson circulation is strongest, and at a different
season than that addressed by . This effect is discussed
with respect to climate change but their arguments could easily be applied to
solar-cycle-induced changes of the sea surface temperature as well.
There is, however, some evidence that weakens the hypothesis of solar-cycle-driven
tropopause temperatures, causing the solar signal in lower
stratospheric water vapour: found a residuum
similar to ours between a combined HALOE and MLS time series and trajectory
calculations on the basis of several reanalysis data sets. Assuming HALOE data
and cold-point temperatures to be correct, this seems to refute the
hypothesis that the only mechanism which connects the solar variability with
the lower stratospheric water vapour content is the variability of cold-point
temperatures with the solar cycle.
Regarding the water vapour trends, there was agreement until recently that
water vapour in the lower stratosphere has increased over the previous
decades .
Only recently, analysed H2O trends of data records
obtained with various space-borne limb-sounding instruments and found
negative trends. Data merging was performed using the Canadian Middle
Atmosphere Model 30 (CMAM30) as a transfer standard.
The different temporal coverage of their and our analysis is a major obstacle
for direct comparison. Nevertheless, they found negative trends of water
vapour in the lower stratosphere in the order of 10 % over 22 years which
is somewhat larger than our values, and they attributed this change mainly to
an intensification of the shallow branch of the Brewer–Dobson circulation.
The analysis performed by was mainly based on the MLS
time series and constructed water vapour abundances applying a trajectory
model on reanalyses. They found that tropical lower stratospheric water
vapour anomalies can fully be described by a multivariate linear regression
including the troposphere temperature at 500 hPa, a QBO proxy and a proxy of
the Brewer–Dobson circulation. With this parametrization no significant
linear trend remains.
The findings by and neither
confirm nor refute our findings. The reasons are these: first, we find it
only natural that trends, which by their nature are a descriptive rather than
an explaining quantity, are found to be different, depending on which explaining
fit parameters are used. Second, the solar cycle might also act upon other
atmospheric quantities, which in turn are correlated with the variation of
water vapour. In particular, solar influence on both the tropospheric
temperature and the Brewer–Dobson circulation was identified (see
) which implies that the parametrization chosen by
has implicitly included a possible solar signal in
water vapour.
Conclusions
A parametric fit of a 20-year time series of lower stratospheric water vapour
based on a merged MIPAS–HALOE data set is improved by inclusion of a solar
cycle term. The water vapour data records within
60∘ S–60∘ N and 15 to 30 km are best described by
including a solar cycle proxy, implying a phase-shifted anti-correlation
between water vapour abundances and solar radiation (i.e. lowest water vapour
after solar maximum). Within the lower stratosphere, this phase shift is
composed of an almost constant inherent time lag of about 25 months and a
variable delay following approximately the age of stratospheric air.
Amplitudes of the solar signal in the water vapour time series are largest
near the tropical tropopause (up to 0.35 ppmv) and decrease with altitude
and latitude. We propose as an explanation of the behaviour of both the
amplitudes and the phase shifts that the solar signal is imprinted on the
water vapour, entering the stratosphere through the tropical tropopause,
possibly restricted to the western Pacific region and is, thus, a consequence
of cold-point temperatures influenced by the solar cycle. The response of
lower stratospheric water vapour to the solar cycle suggests that tropopause
temperatures relevant for the dehydration of air are lowest about 2 years
after solar maximum. Unfortunately, the vertical resolution of conventional
satellite-borne temperature sounders available for the time period under
assessment is not sufficient for the inference of cold-point temperatures,
and radio occultation data have become available only from the year 2000
onwards. Thus, this aspect of our hypothesis cannot be tested.
Inclusion of the solar cycle term in the multivariate linear regression of
the water vapour time series has another important consequence: the linear
term, interpretable as a trend over the 2 decades of observations, becomes
considerably more negative after inclusion of the solar cycle proxy and in
the lower stratosphere the “trend” even changes sign from slightly positive
without the solar proxy term to significantly negative. Thus, including the
solar cycle term as an additional proxy of a driver that rules stratospheric
water vapour has the potential to help to resolve the water vapour conundrum:
increasing water vapour abundances in the tropical and extra-tropical lowest
stratosphere seemed to be in
contradiction with observed constant or even slightly decreasing tropical
tropopause temperatures . The negative net trend derived in our
study could help to solve this.
A robust causal attribution of
the lower stratospheric water vapour fluctuations to solar effects is
admittedly a challenge because of the small temporal coverage of the time
series, which includes less than two solar cycles. But at least it can be
said that in descriptive terms the lower stratospheric water vapour time
series shows a signal which can be well modelled by a solar cycle signal and
whose disregard can affect water vapour trend estimation. Consideration of
other H2O data sources beyond MIPAS and HALOE and the search for a solar
cycle signal in observed cold-point temperatures are suggested as
obvious follow-up activities.